In writing decision letters as a journal editor, I find that I am often making similar observations regarding reported empirics in a study, so I thought I would crystalize my five primary guidelines for evaluating a study.

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I am a fan of John Ioannidis and his work. He’s done a lot to raise attention to the use and abuse of frequentist statistics in, well, lots of the sciences.

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I am not a fan of the necessity to have a novel theoretical contribution to publish in top management journals. Arguably, I think this standard has contributed to the replication crisis in the social sciences. Nonetheless, that is the standard, so it’s helpful to think through just what a theoretical contribution means in the era of the replication crisis.

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Going all in with R

R

Over the years I used, and taught with, mostly Stata, while trying to keep up with R. This year, I’m all in with R.

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As a field, we have a silly devotion to ‘making a theoretical contribution’ as a standard for publication. The necessity for each study to bring something new to the table is the exact opposite of what we should want as scientists—trustworthy, replicable results—that imbue confidence in a model’s predictions.

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This post challenges the assumption that for an academic paper to be relevant it must be interesting, and for the paper to be interesting, it only needs appropriate empirics, as opposed to a rigorous research design and empirical treatment.

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Making a go at blogging (again)!

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